Applications of big data analytics have been used in many fields. In information search and online shopping, people expect to see results instantly and to complete purchase transactions at real-time. Therefore, data computation and transfer should be super fast and real-time. In consumer based products and services industries (like retail, healthcare, and finance), customer retention is a top priority. Predictive modeling as part of big data analytics has been used to predict probabilities of customer churning and to develop strategies to improve customer loyalty. In the healthcare industry, prevention medicine can use big data analytics to predict diseases based on many factors and take immediate preventive actions before diseases occur. In credit card industry, predictive analytics has been used to detect credit card frauds so as to take quick remedy actions.
Since data analytics, especially predictive analytics, is very complex and labor intensive work, it needs deep thinking and specific training and skills in order to put massive amounts of data and business objectives together to find meaningful insights, and develop actionable solutions and smart AI applications. Thanks to modern information technologies which make big data analytics feasible and affordable. The modern information technologies are facilitating analytical results to be quickly produced, instantly interactive, artfully displayed, and intelligently responsive as soon as analytical processes have been developed and implemented.
Automatic online recommendation has been widely used in internet marketing. As users select products to review, other products are displayed on computer screens. This marketing method was initially used at Amazon.com for book sales.
Online product recommendation has been adapted on many websites in order to generate more sales. Many data analysis techniques can generate recommended products lists. Some work well while others may guide to unpleasant routes. Through consumer insights analytics to develop innovative methodologies for better predictions, automatic recommendations can be more effective and helpful in terms of generating greater sales for retailers and also helping customers find more desirable products.
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